Detecting frauds in online advertising systems

Online advertising is aimed to promote and sell products and services of various companies in the global market through internet. In 2005, it was estimated that companies spent $10B in web advertisements, and it is expected to grow by 25-30% in the next few years. The advertisements can be displayed in the search results as sponsored links, on the web sites, etc. Further, these advertisements are personalized based on demographic targeting or on information gained directly from the user. In a standard setting, an advertiser provides the publisher with its advertisements and they agree on some commission for each customer action. This agreement is done in the presence of Internet Advertising commissioners, who represent the middle person between Internet Publishers and Internet Advertisers. The publisher, motivated by the commission paid by the advertisers, displays the advertisers’ links in its search results. Since each player in this scenario can earn huge revenue through this procedure, there is incentive to falsely manipulate the procedure by extracting forbidden information of the customer action. By passing this forbidden information to the other party, one can generate extra revenue. This paper discusses an algorithm for detecting such frauds in web advertising networks

A Taxonomy of Privacy and Security Risks Contributing Factors

Part 2: Privacy MetricsInternational audienceIdentity management system(s) (IDMS) do rely on tokens in order to function. Tokens can contribute to privacy or security risk in IDMS. Specifically, the characteristics of tokens contribute greatly to security and privacy risks in IDMS. Our understanding of how the characteristics of token contribute to privacy and security risks will help us manage the privacy and security risks in IDMS. In this article, we introduce a taxonomy of privacy and security risks contributing factors to improve our understanding of how tokens affect privacy and security in IDMS. The taxonomy is based on a survey of IDMS articles. We observed that our taxonomy can form the basis for a risk assessment model

Subtyping Influenza A Virus with Monoclonal Antibodies and an Indirect Immunofluorescence Assay

Abstract. Any finite tree automaton (or regular type) can be used to construct an abstract interpretation of a logic program, by first determinising and completing the automaton to get a pre-interpretation of the language of the program. This has been shown to be a flexible and practical approach to building a variety of analyses, both generic (such as mode analysis) and program-specific (with respect to a type describing some particular property of interest). Previous work demonstrated the approach using pre-interpretations over small domains. In this paper we present techniques that allow the method to be applied to more complex pre-interpretations and larger programs. There are two main techniques presented: the first is a novel algorithm for determinising finite tree automata, yielding a compact “product” form of the transitions of the result automaton, that is often orders of magnitude smaller than an explicit representation of the automaton. Secondly, it is shown how this form (which is a representation of a pre-interpretation) can then be input directly to a BDD-based analyser of Datalog programs. We demonstrate through experiments that much more complex analyses become feasible.

Using Datalog with binary decision diagrams for program analysis

Many problems in program analysis can be expressed naturally and concisely in a declarative language like Datalog. This makes it easy to specify new analyses or extend or compose existing analyses. However, previous implementations of declarative languages perform poorly compared with traditional implementations. This paper describes bddbddb, a BDD-Based Deductive DataBase, which implements the declarative language Datalog with stratified negation, totally-ordered finite domains and comparison operators. bddbddb uses binary decision diagrams (BDDs) to efficiently represent large relations. BDD operations take time proportional to the size of the data structure, not the number of tuples in a relation, which leads to fast execution times. bddbddb is an effective tool for implementing a large class of program analyses. We show that a context-insensitive points-to analysis implemented with bddbddb is about twice as fast as a carefully hand-tuned version. The use of BDDs also allows us to solve heretofore unsolved problems, like context-sensitive pointer analysis for large programs

W18 - PDMST '07 & GRep '07: 4th international workshop on P2P Data Management, Security, and Trust: 3rd international workshop on Data management in Global data Repositories

10.1109/DEXA.2007.4312999Proceedings - International Workshop on Database and Expert Systems Applications, DEXA775-77